is used as the estimation model to predict future locations of each detected object based on their current location for better association, smoothing trajectories, and predict missed tracks. The trajectories are further analyzed to monitor the motion patterns of the detected road-users in terms of location, speed, and moving direction. This explains the concept behind the working of Step 3. They do not perform well in establishing standards for accident detection as they require specific forms of input and thereby cannot be implemented for a general scenario. . In later versions of YOLO [22, 23] multiple modifications have been made in order to improve the detection performance while decreasing the computational complexity of the method. Work fast with our official CLI. The probability of an accident is determined based on speed and trajectory anomalies in a vehicle after an overlap with other vehicles. The overlap of bounding boxes of vehicles, Determining Trajectory and their angle of intersection, Determining Speed and their change in acceleration. The probability of an Computer Vision-based Accident Detection in Traffic Surveillance - Free download as PDF File (.pdf), Text File (.txt) or read online for free. If (L H), is determined from a pre-defined set of conditions on the value of . Based on this angle for each of the vehicles in question, we determine the Change in Angle Anomaly () based on a pre-defined set of conditions. We can observe that each car is encompassed by its bounding boxes and a mask. The next criterion in the framework, C3, is to determine the speed of the vehicles. Then, the Acceleration (A) of the vehicle for a given Interval is computed from its change in Scaled Speed from S1s to S2s using Eq. are analyzed in terms of velocity, angle, and distance in order to detect of IEE Colloquium on Electronics in Managing the Demand for Road Capacity, Proc. If the pair of approaching road-users move at a substantial speed towards the point of trajectory intersection during the previous. We can minimize this issue by using CCTV accident detection. become a beneficial but daunting task. This is a cardinal step in the framework and it also acts as a basis for the other criteria as mentioned earlier. vehicle-to-pedestrian, and vehicle-to-bicycle. A score which is greater than 0.5 is considered as a vehicular accident else it is discarded. This is accomplished by utilizing a simple yet highly efficient object tracking algorithm known as Centroid Tracking [10]. Therefore, computer vision techniques can be viable tools for automatic accident detection. A sample of the dataset is illustrated in Figure 3. All programs were written in Python3.5 and utilized Keras2.2.4 and Tensorflow1.12.0. The next task in the framework, T2, is to determine the trajectories of the vehicles. Figure 4 shows sample accident detection results by our framework given videos containing vehicle-to-vehicle (V2V) side-impact collisions. The first part takes the input and uses a form of gray-scale image subtraction to detect and track vehicles. All the data samples that are tested by this model are CCTV videos recorded at road intersections from different parts of the world. What is Accident Detection System? This framework was evaluated on diverse The third step in the framework involves motion analysis and applying heuristics to detect different types of trajectory conflicts that can lead to accidents. 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Farhadi, You only look once: unified, real-time object detection, Proceedings of the IEEE conference on computer vision and pattern recognition, Anomalous driving detection for traffic surveillance video analysis, 2021 IEEE International Conference on Imaging Systems and Techniques (IST), H. Shi, H. Ghahremannezhadand, and C. Liu, A statistical modeling method for road recognition in traffic video analytics, 2020 11th IEEE International Conference on Cognitive Infocommunications (CogInfoCom), A new foreground segmentation method for video analysis in different color spaces, 24th International Conference on Pattern Recognition, Z. Tang, G. Wang, H. Xiao, A. Zheng, and J. Hwang, Single-camera and inter-camera vehicle tracking and 3d speed estimation based on fusion of visual and semantic features, Proceedings of the IEEE conference on computer vision and pattern recognition workshops, A vision-based video crash detection framework for mixed traffic flow environment considering low-visibility condition, L. Yue, M. Abdel-Aty, Y. Wu, O. Zheng, and J. Yuan, In-depth approach for identifying crash causation patterns and its implications for pedestrian crash prevention, Computer Vision-based Accident Detection in Traffic Surveillance, Artificial Intelligence Enabled Traffic Monitoring System, Incident Detection on Junctions Using Image Processing, Automatic vehicle trajectory data reconstruction at scale, Real-time Pedestrian Surveillance with Top View Cumulative Grids, Asynchronous Trajectory Matching-Based Multimodal Maritime Data Fusion However, there can be several cases in which the bounding boxes do overlap but the scenario does not necessarily lead to an accident. By taking the change in angles of the trajectories of a vehicle, we can determine this degree of rotation and hence understand the extent to which the vehicle has underwent an orientation change. consists of three hierarchical steps, including efficient and accurate object after an overlap with other vehicles. After the object detection phase, we filter out all the detected objects and only retain correctly detected vehicles on the basis of their class IDs and scores. The framework is built of five modules. This framework was evaluated on diverse conditions such as broad daylight, low visibility, rain, hail, and snow using the proposed dataset. The dataset is publicly available This is done for both the axes. Register new objects in the field of view by assigning a new unique ID and storing its centroid coordinates in a dictionary. The velocity components are updated when a detection is associated to a target. based object tracking algorithm for surveillance footage. The bounding box centers of each road-user are extracted at two points: (i) when they are first observed and (ii) at the time of conflict with another road-user. of the proposed framework is evaluated using video sequences collected from Abstract: In Intelligent Transportation System, real-time systems that monitor and analyze road users become increasingly critical as we march toward the smart city era. The variations in the calculated magnitudes of the velocity vectors of each approaching pair of objects that have met the distance and angle conditions are analyzed to check for the signs that indicate anomalies in the speed and acceleration. Hence, effectual organization and management of road traffic is vital for smooth transit, especially in urban areas where people commute customarily. to detect vehicular accidents used the feed of a CCTV surveillance camera by generating Spatio-Temporal Video Volumes (STVVs) and then extracting deep representations on denoising autoencoders in order to generate an anomaly score while simultaneously detecting moving objects, tracking the objects, and then finding the intersection of their tracks to finally determine the odds of an accident occurring. Dhananjai Chand2, Savyasachi Gupta 3, Goutham K 4, Assistant Professor, Department of Computer Science and Engineering, B.Tech., Department of Computer Science and Engineering, Results, Statistics and Comparison with Existing models, F. Baselice, G. Ferraioli, G. Matuozzo, V. Pascazio, and G. Schirinzi, 3D automotive imaging radar for transportation systems monitoring, Proc. In this paper, a neoteric framework for detection of road accidents is proposed. 8 and a false alarm rate of 0.53 % calculated using Eq. An accident Detection System is designed to detect accidents via video or CCTV footage. This work is evaluated on vehicular collision footage from different geographical regions, compiled from YouTube. Since most intersections are equipped with surveillance cameras automatic detection of traffic accidents based on computer vision technologies will mean a great deal to traffic monitoring systems. Surveillance Cameras, https://lilianweng.github.io/lil-log/assets/images/rcnn-family-summary.png, https://www.asirt.org/safe-travel/road-safety-facts/, https://www.cdc.gov/features/globalroadsafety/index.html. The proposed framework provides a robust method to achieve a high Detection Rate and a low False Alarm Rate on general road-traffic CCTV surveillance footage. Therefore, Experimental evaluations demonstrate the feasibility of our method in real-time applications of traffic management. Section III delineates the proposed framework of the paper. Though these given approaches keep an accurate track of motion of the vehicles but perform poorly in parametrizing the criteria for accident detection. The following are the steps: The centroid of the objects are determined by taking the intersection of the lines passing through the mid points of the boundary boxes of the detected vehicles. A popular . Real-time Near Accident Detection in Traffic Video, COLLIDE-PRED: Prediction of On-Road Collision From Surveillance Videos, Deep4Air: A Novel Deep Learning Framework for Airport Airside Typically, anomaly detection methods learn the normal behavior via training. accident detection by trajectory conflict analysis. The proposed framework consists of three hierarchical steps, including . Automatic detection of traffic accidents is an important emerging topic in Section II succinctly debriefs related works and literature. This paper introduces a solution which uses state-of-the-art supervised deep learning framework. As a result, numerous approaches have been proposed and developed to solve this problem. detect anomalies such as traffic accidents in real time. This parameter captures the substantial change in speed during a collision thereby enabling the detection of accidents from its variation. method to achieve a high Detection Rate and a low False Alarm Rate on general Of gray-scale image subtraction to detect accidents via video or CCTV footage of intersection... Of bounding boxes and a false alarm Rate of 0.53 % calculated using Eq observe each! Therefore, Experimental evaluations demonstrate the feasibility of our method in real-time of... Field of view by assigning a new unique ID and storing its coordinates! 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